Advancing Photometric Odometry to Dense Volumetric Simultaneous Localization and Mapping

Abstract

Navigating complex environments remains a fundamental challenge in robotics. At the core of this challenge is Simultaneous Localization and Mapping (SLAM), the process of creating a map of the environment while simultaneously using that map for navigation. SLAM is essential for mobile robotics because effective navigation is a prerequisite for nearly all real-world robotic applications. Visual SLAM, which relies solely on the input of RGB cameras is important because of the accessibility of cameras, which makes it an ideal solution for widespread robotic deployment.

Recent advances in graphics have driven innovation in the visual SLAM domain. Techniques like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) enable the rapid generation of dense volumetric scenes from RGB images. Researchers have integrated these radiance field techniques into SLAM to address a key limitation of traditional systems. Although traditional SLAM excels at localization, the generated maps are often unsuitable for broader robotics applications. By incorporating radiance fields, SLAM systems have the potential for the real-time creation of volumetric metric-semantic maps, offering substantial benefits for robotics. However, current radiance field-based SLAM approaches face challenges, particularly in processing speed and map reconstruction quality.

This work introduces a solution that addresses limitations in current radiance fields SLAM systems. Direct SLAM, a traditional SLAM technique, shares key operational similarities with radiance field approaches that suggest potential synergies between the two systems. Both methods rely on photometric loss optimization, where the pixel differences between images guide the optimization process. This work demonstrates that the benefits of combining these complementary techniques extend beyond theory.

This work demonstrates the synergy between radiance field techniques and direct SLAM through a novel system that combines 3DGS with direct SLAM, achieving a superior combination of quality, memory efficiency, and speed compared to existing approaches. The system, named MGSO, addresses a challenge in current 3DGS SLAM systems: Initializing 3D Gaussians while performing SLAM simultaneously. The proposed approach leverages direct SLAM to produce dense and structured point clouds for 3DGS initialization. This results in faster optimization, memory compactness, and higher-quality maps even with mobile hardware. These results demonstrate that traditional direct SLAM techniques can be effectively integrated with radiance field representations, opening avenues for future research.